Formation of Object Representations over TimeAs we look about us, it seems as though the perception of our environment and its objects is full of detail, highly accurate, and instantaneous. However, in order to arrive at this seemingly clear and complete impression, our visual systems are continuously processing the incoming information and adjusting our perceptual experience. For most objects that we see, this does occur very quickly, but not instantaneously. For illusory contours and cases of dynamic occlusion in which only parts of an object are visible at any given time, the complete form must arrive gradually and missing fragments of the shape must be filled in over time. Similar processes are at work even when the entire object is visible all at once. Using a combination of source-localized EEG and fMRI, we are currently investigating the temporal interplay between visual areas during the construction of a shape percept. We find that in the first hundreds of milliseconds of a dynamic display, there is a flow of activity between early, intermediate, and ventral visual areas, forward and backward, suggesting that shape percepts are gradually built up and then refined over time.

​Role of Dorsal Stream in Shape PerceptionClassically, perception has been thought of as proceeding along two processing streams or pathways: the ventral or "what" pathway which is involved in object recognition and the dorsal or "where" or "how" pathway which deals with spatial processing. However, several studies have recently suggested that shape information is not relegated to ventral visual areas (hV4, LOC) and that intermediate and dorsal regions (V3A/B, IPS) may be critical in forming representations of objects, particularly when they are in motion or when object fragments must be integrated across space and time to form perceptual units (spatiotemporal integration). Are these regions causally involved in dynamic shape representation, shape integration and interpolation, or shape representation in general? How are shape and motion information integrated? We have found that shape identity information is indeed represented in dorsal visual areas and that the degree of communication and feedback between dorsal, intermediate, and ventral visual areas has been greatly underestimated.

Shape Representation in Deep Convolutional Neural Networks​Great progress has been made in the last 10 years in techniques for performing image categorization tasks, largely due to the advent of deep neural networks (and the computing power necessary to implement them). In the human vision community, there is growing interest in these models because they seem to share many of the same features and organization that are thought to exist in the human visual system. In collaboration with the Kellman and Lu labs at UCLA, we have begun to explore the kinds of features extracted by deep networks and have shown that they diverge in important ways from human behavior. Much greater caution is warranted in making analogies between such artificial systems and human vision.